/*=========================================================================

  Program:   ORFEO Toolbox
  Language:  C++
  Date:      $Date$
  Version:   $Revision$


  Copyright (c) Centre National d'Etudes Spatiales. All rights reserved.
  See OTBCopyright.txt for details.


     This software is distributed WITHOUT ANY WARRANTY; without even
     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
     PURPOSE.  See the above copyright notices for more information.

=========================================================================*/

//  Software Guide : BeginCommandLineArgs
//    INPUTS: {QB_Suburb.png}
//    OUTPUTS: {NeighborhoodIterators4a.png}
//    0
//  Software Guide : EndCommandLineArgs
//  Software Guide : BeginCommandLineArgs
//    INPUTS: {QB_Suburb.png}
//    OUTPUTS: {NeighborhoodIterators4b.png}
//    1
//  Software Guide : EndCommandLineArgs
//  Software Guide : BeginCommandLineArgs
//    INPUTS: {QB_Suburb.png}
//    OUTPUTS: {NeighborhoodIterators4c.png}
//    2
//  Software Guide : EndCommandLineArgs
//  Software Guide : BeginCommandLineArgs
//    INPUTS: {QB_Suburb.png}
//    OUTPUTS: {NeighborhoodIterators4d.png}
//    5
//  Software Guide : EndCommandLineArgs

#include "otbImage.h"
#include "otbImageFileReader.h"
#include "otbImageFileWriter.h"
#include "itkUnaryFunctorImageFilter.h"
#include "itkRescaleIntensityImageFilter.h"
#include "itkConstNeighborhoodIterator.h"
#include "itkImageRegionIterator.h"
#include "itkNeighborhoodAlgorithm.h"
#include "itkNeighborhoodInnerProduct.h"

// Software Guide : BeginLatex
//
// We now introduce a variation on convolution filtering that is useful when a
// convolution kernel is separable.  In this example, we create a different
// neighborhood iterator for each axial direction of the image and then take
// separate inner products with a 1D discrete Gaussian kernel.
// The idea of using several neighborhood iterators at once has applications
// beyond convolution filtering and may improve efficiency when the size of
// the whole neighborhood relative to the portion of the neighborhood used
// in calculations becomes large.
//
// The only new class necessary for this example is the Gaussian operator.
// Software Guide : EndLatex

// Software Guide : BeginCodeSnippet
#include "itkGaussianOperator.h"
// Software Guide : EndCodeSnippet

int main(int argc, char *argv[])
{
  if (argc < 4)
    {
    std::cerr << "Missing parameters. " << std::endl;
    std::cerr << "Usage: " << std::endl;
    std::cerr << argv[0]
              << " inputImageFile outputImageFile sigma"
              << std::endl;
    return -1;
    }

  typedef float                           PixelType;
  typedef otb::Image<PixelType, 2>        ImageType;
  typedef otb::ImageFileReader<ImageType> ReaderType;

  typedef itk::ConstNeighborhoodIterator<ImageType> NeighborhoodIteratorType;
  typedef itk::ImageRegionIterator<ImageType>       IteratorType;

  ReaderType::Pointer reader = ReaderType::New();
  reader->SetFileName(argv[1]);
  try
    {
    reader->Update();
    }
  catch (itk::ExceptionObject& err)
    {
    std::cout << "ExceptionObject caught !" << std::endl;
    std::cout << err << std::endl;
    return -1;
    }

  ImageType::Pointer output = ImageType::New();
  output->SetRegions(reader->GetOutput()->GetRequestedRegion());
  output->Allocate();

  itk::NeighborhoodInnerProduct<ImageType> innerProduct;

  typedef itk::NeighborhoodAlgorithm
  ::ImageBoundaryFacesCalculator<ImageType> FaceCalculatorType;

  FaceCalculatorType                         faceCalculator;
  FaceCalculatorType::FaceListType           faceList;
  FaceCalculatorType::FaceListType::iterator fit;

  IteratorType             out;
  NeighborhoodIteratorType it;

// Software Guide : BeginLatex
// The Gaussian operator, like the Sobel operator, is instantiated with a pixel
// type and a dimensionality.  Additionally, we set the variance of the
// Gaussian, which has been read from the command line as standard deviation.
// Software Guide : EndLatex

// Software Guide : BeginCodeSnippet
  itk::GaussianOperator<PixelType, 2> gaussianOperator;
  gaussianOperator.SetVariance(::atof(argv[3]) * ::atof(argv[3]));
// Software Guide : EndCodeSnippet

// Software Guide : BeginLatex
//
// The only further changes from the previous example are in the main loop.
// Once again we use the results from face calculator to construct a loop that
// processes boundary and non-boundary image regions separately.  Separable
// convolution, however, requires an additional, outer loop over all the image
// dimensions.  The direction of the Gaussian operator is reset at each
// iteration of the outer loop using the new dimension.  The iterators change
// direction to match because they are initialized with the radius of the
// Gaussian operator.
//
// Input and output buffers are swapped at each iteration so that the output of
// the previous iteration becomes the input for the current iteration. The swap
// is not performed on the last iteration.
//
// Software Guide : EndLatex

// Software Guide : BeginCodeSnippet
  ImageType::Pointer input = reader->GetOutput();
  for (unsigned int i = 0; i < ImageType::ImageDimension; ++i)
    {
    gaussianOperator.SetDirection(i);
    gaussianOperator.CreateDirectional();

    faceList = faceCalculator(input, output->GetRequestedRegion(),
                              gaussianOperator.GetRadius());

    for (fit = faceList.begin(); fit != faceList.end(); ++fit)
      {
      it = NeighborhoodIteratorType(gaussianOperator.GetRadius(),
                                    input, *fit);

      out = IteratorType(output, *fit);

      for (it.GoToBegin(), out.GoToBegin(); !it.IsAtEnd(); ++it, ++out)
        {
        out.Set(innerProduct(it, gaussianOperator));
        }
      }

    // Swap the input and output buffers
    if (i != ImageType::ImageDimension - 1)
      {
      ImageType::Pointer tmp = input;
      input = output;
      output = tmp;
      }
    }
// Software Guide : EndCodeSnippet

// Software Guide : BeginLatex
//
// The output is rescaled and written as in the previous examples.
// Figure~\ref{fig:NeighborhoodExample4} shows the results of Gaussian blurring
// the image \code{Examples/Data/QB\_Suburb.png} using increasing
// kernel widths.
//
// \begin{figure}
// \centering
// \includegraphics[width=0.23\textwidth]{NeighborhoodIterators4a.eps}
// \includegraphics[width=0.23\textwidth]{NeighborhoodIterators4b.eps}
// \includegraphics[width=0.23\textwidth]{NeighborhoodIterators4c.eps}
// \includegraphics[width=0.23\textwidth]{NeighborhoodIterators4d.eps}
// \itkcaption[Gaussian blurring by convolution filtering]{Results of
// convolution filtering with a Gaussian kernel of increasing standard
// deviation $\sigma$ (from left to right, $\sigma = 0$, $\sigma = 1$, $\sigma
// = 2$, $\sigma = 5$).  Increased blurring reduces contrast and changes the
// average intensity value of the image, which causes the image to appear
// brighter when rescaled.}
// \protect\label{fig:NeighborhoodExample4}
// \end{figure}
//
// Software Guide : EndLatex

  typedef unsigned char                        WritePixelType;
  typedef otb::Image<WritePixelType, 2>        WriteImageType;
  typedef otb::ImageFileWriter<WriteImageType> WriterType;

  typedef itk::RescaleIntensityImageFilter<
      ImageType, WriteImageType> RescaleFilterType;

  RescaleFilterType::Pointer rescaler = RescaleFilterType::New();

  rescaler->SetOutputMinimum(0);
  rescaler->SetOutputMaximum(255);
  rescaler->SetInput(output);

  WriterType::Pointer writer = WriterType::New();
  writer->SetFileName(argv[2]);
  writer->SetInput(rescaler->GetOutput());
  try
    {
    writer->Update();
    }
  catch (itk::ExceptionObject& err)
    {
    std::cout << "ExceptionObject caught !" << std::endl;
    std::cout << err << std::endl;
    return -1;
    }

  return EXIT_SUCCESS;
}
